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Article

Understanding Spatiotemporal Variations of Ridership by Multiple Taxi Services

1
School of Transportation, Southeast University, Nanjing 211189, China
2
Department of Civil Engineering, Monash University, Clayton, VIC 3800, Australia
3
Lyles School of Civil Engineering, Purdue University, W. Lafayette, IN 47907, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(12), 757; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120757
Received: 17 November 2020 / Revised: 15 December 2020 / Accepted: 18 December 2020 / Published: 18 December 2020
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
Recent years have seen the big growth of app-based taxi services by not only competing for rides with street-hailing taxi services but also generating new taxi rides. Moreover, the innovation in dynamic pricing also makes it competitive in both passenger and driver sides. However, current literature still lacks better understandings of induced changes in spatiotemporal variations in multiple taxi ridership after app-based taxi service launch. This study develops two study cases in New York City to explore impacts of presence of app-based taxi services on daily total and street-hailing taxi rides and impacts of dynamic pricing on hourly app-based taxi rides. Considering the panel data and treatment effect measurement in this problem, we introduce a mixed modeling structure with both geographically weighted panel regression and difference-in-difference estimator. This mixed modeling structure outperforms traditional fixed effects model in our study cases. Empirical analyses identified the significant spatiotemporal variations in impacts of presence of app-based taxi services; for instance, impacts daily total taxi rides in 2014 and 2016 and impacts on street-hailing taxi rides from 2012 to 2016. Moreover, we capture the spatial variations in impacts of dynamic pricing on hourly app-based taxi rides, as well as significant impacts of time of day, day of week, and vehicle supply. View Full-Text
Keywords: app-based taxi services; treatment effects; geographically weighted panel regression; taxi ridership app-based taxi services; treatment effects; geographically weighted panel regression; taxi ridership
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MDPI and ACS Style

Zhang, W.; Xi, Y.; Ukkusuri, S.V. Understanding Spatiotemporal Variations of Ridership by Multiple Taxi Services. ISPRS Int. J. Geo-Inf. 2020, 9, 757. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120757

AMA Style

Zhang W, Xi Y, Ukkusuri SV. Understanding Spatiotemporal Variations of Ridership by Multiple Taxi Services. ISPRS International Journal of Geo-Information. 2020; 9(12):757. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120757

Chicago/Turabian Style

Zhang, Wenbo, Yinfei Xi, and Satish V. Ukkusuri 2020. "Understanding Spatiotemporal Variations of Ridership by Multiple Taxi Services" ISPRS International Journal of Geo-Information 9, no. 12: 757. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120757

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